zama-ai/concrete-ml

Health v2 · maintenance only

Flags: no_license

Adoption not part of the score

1445 stars · 203 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Concrete ML is a privacy-preserving machine learning library built on top of Zama's Concrete FHE compiler. It lets data scientists convert scikit-learn-style models and PyTorch neural networks into fully homomorphic encryption equivalents for inference or training on encrypted data without cryptography expertise.

Use cases

• run machine learning inference on encrypted data
• train models without exposing sensitive training data
• analyze healthcare data while preserving patient privacy
• convert scikit-learn models to FHE equivalents
• deploy PyTorch models that operate on encrypted inputs
• build privacy-compliant ML services under strict data regulations

When to choose

• you need ML on encrypted data without decrypting it
• your models use scikit-learn, XGBoost, or PyTorch APIs
• data privacy regulations prevent plaintext processing
• you want FHE without writing cryptography code

When to avoid

• you need low-latency inference, as FHE adds large overhead
• your models rely on operations unsupported by FHE quantization
• you only need standard ML without privacy constraints
• you need a non-Python stack

Facets

library · maturity active

machine-learning cryptography privacy security machine-learning privacy data-science security python fhe fully-homomorphic-encryption ppml scikit-learn pytorch encrypted-inference privacy-preserving-ml

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For agents

markdown · JSON · MCP: product_card(name="zama-ai/concrete-ml")